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Mihai Barbuceanu

dblp:15/4566 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 2003
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Programming languages and type systems · 100%
Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Programming languages and type systems › programming paradigms
declarative programming
0.011987
Integrating Declarative Knowledge Programming Styles and Tools in a Structured Object AI Environment · IJCAI 1987
YearPublicationVenuePosition
2003 Building Agents for the Customer Service Front
Mihai Barbuceanu, Mark S. Fox, Lei Hong, Yannick Lallement, Zhongdong Zhang
IAAI1
2000 Integrating Individual, Organizational and Market Level Reasioning for Agent Coordination
Mihai Barbuceanu, Wai-Kau Lo
ECAI1
1996 Capturing and Modeling Coordination Knowledge for Multi-Agent Systems
abstract
The agent view provides a level of abstraction at which we envisage computational systems carrying out cooperative work by interoperating globally across networks connecting people, organizations and machines. A major challenge in building such systems is coordinating the behavior of the individual agents to achieve the individual and shared goals of the participants. As part of a larger project targeted at developing an Agent Building Shell for multiagent applications, we have designed and implemented a coordination language aimed at explicitly representing, applying and capturing coordination knowledge for multiagent systems. The language provides KQML-based communication, an agent definition and execution environment, support for modeling interactions as multiple structured conversations among agents, rule-based approaches to conversation selection and execution, as well as an interactive tool for in context acquisition and debugging of cooperation knowledge. The paper presents these components in detail and then shows how the coordination language is used in the Agent Building Shell to manage content-based information distribution scenarios among agents and the coordination aspects of conflict management processes that occur when agents encounter inconsistencies. The major application of the system is the construction and integration of multiagent supply chain systems for manufacturing enterprises. This application is used throughout the paper to illustrate the introduced concepts and language constructs.
Mihai Barbuceanu, Mark S. Fox
Int. J. Cooperative Inf. Syst.1
1987 Integrating Declarative Knowledge Programming Styles and Tools in a Structured Object AI Environment
Mihai Barbuceanu, Stefan Trausan-Matu, Bálint Molnár
IJCAI1